Real-Time Industrial Sensor Fault Diagnosis via Global Correlation Modeling and Feature Attention-Optimized Network

To address diagnostic delays and high false-alarm rates in multi-sensor systems, this paper proposes MIC‑GAEAN, a lightweight network for anomaly detection. The framework constructs a global sensor correlation network using the Maximal Information Coefficient (MIC) to capture complex dependencies, which is dynamically refined via a lightweight attention mechanism. Notably, training excludes the target sensor’s own historical data to prevent information leakage and enhance generalization. An adaptive MLP then performs point‑level anomaly detection through single‑step prediction. A novel Correlation Compensation Mechanism further uses healthy sensor data to set theoretical norms, distinguishing faults from normal variations and reducing false alarms. Validated on industrial data, MIC‑GAEAN demonstrates high real‑time accuracy, efficiency, and suitability for data‑scarce, long‑interval monitoring, offering a reliable solution for industrial system reliability.